On Generating Lagrangian Cuts for Two-Stage Stochastic Integer Programs

TL;DR

This paper introduces new techniques for generating Lagrangian cuts in two-stage stochastic integer programs, significantly improving relaxation strength and computational efficiency compared to previous methods.

Contribution

The paper proposes novel methods for efficiently generating Lagrangian cuts that enhance Benders relaxation in stochastic integer programming.

Findings

Proposed methods improve Benders relaxation faster than previous approaches.

Using these cuts reduces the size of the search tree in branch-and-cut algorithms.

Computational results show significant performance gains on test problems.

Abstract

We investigate new methods for generating Lagrangian cuts to solve two-stage stochastic integer programs. Lagrangian cuts can be added to a Benders reformulation, and are derived from solving single scenario integer programming subproblems identical to those used in the nonanticipative Lagrangian dual of a stochastic integer program. While Lagrangian cuts have the potential to significantly strengthen the Benders relaxation, generating Lagrangian cuts can be computationally demanding. We investigate new techniques for generating Lagrangian cuts with the goal of obtaining methods that provide significant improvements to the Benders relaxation quickly. Computational results demonstrate that our proposed method improves the Benders relaxation significantly faster than previous methods for generating Lagrangian cuts and, when used within a branch-and-cut algorithm, significantly reduces the size of the search tree for three classes of test problems.